SoroushVahidi/scidocs huggingface dataset:
A Design and Assessment of a Direction Finding Proximity Fuze Sensor
(2013)

This paper presents the implementation and assessment of a direction finding proximity fuze sensor for anti-aircrafts or anti-air missiles. A higher rejection of clutter signals is achieved by employing a binary phase shift keying modulation using Legendre sequence. The direction finding is implemented by comparing received powers from six receiving antennas equally spaced by an angle of 60° around a cylindrical surface. In addition, target detection algorithms have been developed for a robust detection of the target, taking the wide variation of target related parameters into considerations. The performances of the developed fuze sensor are experimentally verified by constructing the fuze-specific encounter simulation test apparatus, which collects and analyzes reflected signals from a standard target. The developed fuze sensor can successfully detect the -10 dBsm target over a 10 m range as well as the direction with an out-of-range rejection of about 40 dB. Furthermore, the developed fuze sensor can clearly detect the target with mesh clutter environment. To assess realistic operation, the fuze sensor is tested using 155 mm gun firing test setup. Through the gun firing test, the successful fuzing range and direction finding performances are demonstrated.
Cognitive Context Models and Discourse
(1998)

1. Mental models Since the early 1980s, the notion of mental model has been quite successful in cognitive psychology in general, and in the theory of text processing in particu-Such models have been conceptualized as representations in episodic memory of situations, acts or events spoken or thought about, observed or participated in by human actors, that is of experiences (Ehrlich, Tardieu & Cavazza 1993). In the theory of text processing, such (situation or event) models played a crucial role in establishing the necessary referential basis for the processing of anaphora and other phenomena of coherence (Albrecht & O Brien 1993). They further explained, among many other things, why text recall does not seem to be based on semantic representations of texts, but rather on the mental model construed or updated of the event the text is about (Bower & Morrow 1990). Conversely, mental models also play a role in the much neglected theory of discourse production, viz., as the mental point of departure of all text and talk, from which relevant information may be selected for the strategic construction of their global and local semantic structures. Many experiments have confirmed these hypotheses, and have shown at text comprehension and recall essentially involve a strategic manipulation of models, for instance by matching text information with structures of the The notion of mental space is sometimes also used in formal linguistics as a construct that has similar functions as our notion of a mental model (Faucormier 1985).
XBOS: An Extensible Building Operating System
(2015)

We present XBOS, an eXtensible Building Operating System for integrated management of previously isolated building subsystems. The key contribution of XBOS is the development of the Building Profile, a canonical, executable description of a building and its subsystems that changes with a building and evolves the control processes and applications accordingly. We discuss the design and implementation of XBOS in the context of 6 dimensions of an effective BAS – hardware presentation layer, canonical metadata, control process management, building evolution management, security, scalable UX and API – and evaluate against several recent building and sensor management systems. Lastly, we examine the evolution of a real, 10 month deployment of XBOS in a 7000 sq ft office building.
Survey on Collaborative AR for Multi-user in Urban Simulation
(2009)

This paper describes an augmented reality (AR) environment that allows multiple participants or multi-user to interact with 2D and 3D data. AR simply can provide a collaborative interactive AR environment for urban simulation, where users can interact naturally and intuitively. In addition, the collaborative AR makes multi-user in urban simulation to share simultaneously a real world and virtual world. The fusion between real and virtual world, existed in AR environment by see-through HMDs, achieves higher interactivity as a key features of collaborative AR. In real-time, precise registration between both worlds and multiuser are crucial for the collaborations. Collaborative AR allow multi-user to simultaneously share a real world surrounding them and a virtual world. Common problems in AR environment will be discussed and major issues in collaborative AR will be explained details in this paper. The features of collaboration in AR environment are will be identified and the requirements of collaborative AR will be defined. This paper will give an overview on collaborative AR environment for multi-user in urban studies and planning. The work will also cover numerous systems of collaborative AR environments for multi-user.
Training Deep and Recurrent Networks with Hessian-Free Optimization
(2012)

Hessian-Free optimization (HF) is an approach for unconstrained minimization of real-valued smooth objective functions. Like standard Newton’s method, it uses local quadratic approximations to generate update proposals. It belongs to the broad class of approximate Newton methods that are practical for problems of very high dimensionality, such as the training objectives of large neural networks. Different algorithms that use many of the same key principles have appeared in the literatures of various communities under different names such as Newton-CG, CG-Steihaug, Newton-Lanczos, and Truncated Newton [27, 28], but applications to machine learning and especially neural networks, have been limited or non-existent until recently. With the work of Martens [22] and later Martens and Sutskever [23] it has been demonstrated that such an approach, if carefully designed and implemented, can work very well for optimizing nonconvex functions such as the training objective for deep neural networks and recurrent neural networks (RNNs), given sensible random initializations. This was significant because gradient descent methods have been observed to be very slow and sometimes completely ineffective [16, 4, 17] on these problems, unless special non-random initializations schemes like layer-wise pre-training [16, 15, 3] are used. HF, which is a general optimization-based approach, can be used in conjunction with or as an alternative to existing pre-training methods and is more widely applicable, since it relies on fewer assumptions about the specific structure of the network. In this report we will first describe the basic HF approach, and then examine well-known general purpose performance-improving techniques as well as others that are specific to HF (versus other Truncated-Newton type approaches) or to neural networks. We will also provide practical tips for creating efficient and correct implementations, and discuss the pitfalls which may arise when designing and using an HF-based approach in a particular application.
Neuronlike adaptive elements that can solve difficult learning control problems
(1983)

It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation
(2009)

We introduce the first temporal-difference learning algorithms that converge with smooth value function approximators, such as neural networks. Conventional temporal-difference (TD) methods, such as TD(λ), Q-learning and Sarsa have been used successfully with function approximation in many applications. However, it is well known that off-policy sampling, as well as nonlinear function approximation, can cause these algorithms to become unstable (i.e., the parameters of the approximator may diverge). Sutton et al. (2009a, 2009b) solved the problem of off-policy learning with linear TD algorithms by introducing a new objective function, related to the Bellman error, and algorithms that perform stochastic gradient-descent on this function. These methods can be viewed as natural generalizations to previous TD methods, as they converge to the same limit points when used with linear function approximation methods. We generalize this work to nonlinear function approximation. We present a Bellman error objective function and two gradient-descent TD algorithms that optimize it. We prove the asymptotic almost-sure convergence of both algorithms, for any finite Markov decision process and any smooth value function approximator, to a locally optimal solution. The algorithms are incremental and the computational complexity per time step scales linearly with the number of parameters of the approximator. Empirical results obtained in the game of Go demonstrate the algorithms’ effectiveness.
A 0.5V supply, 49nW band-gap reference and crystal oscillator in 40nm CMOS
(2017)

This paper presents the co-design of 0.5V operational band-gap reference (BGR) and 32kHz crystal oscillator (XO) in 40nm CMOS process. The proposed BJT-based BGR offers 10× resistor area reduction compared to the conventional BGR. The proposed XO provides trans-conductance enhancement without the need for large coupling capacitor and uses a feedback loop to regulate its bias current, oscillation amplitude across PVT. The total power consumption is less than 49nW from −40°C to 120°C. Without any trimming, the BGR achieves temperature coefficients of 8ppm/°C and XO has temperature coefficient of 0.25ppm/°C. The BGR offers 5× lower area, 2× lower power consumption at 120°C compared to prior work on resistor-based, non-duty cycled BGR. The XO improves power consumption by at least 2× compared to prior work at 120°C, 0.5V supply.
Multiobjective Optimization of Ultraflat Magnetic Components With PCB-Integrated Core
(2013)

In future applications, e.g., in ultra-flat OLED lamp drivers or flat screen power supplies, ultra-flat ac/dc and dc/dc converter systems are highly demanded. Therefore, the design and implementation of a printed circuit board (PCB)-integrated flyback transformer for a 1-mm-thin single-phase power factor correction rectifier is under investigation. In this paper, first an overview on several integration methods is given. It is shown that the PCB integration of magnetic cores allows us to achieve the required thickness of 1 mm and a high energy density. In a next step, the design and the realization of ultra-flat magnetic components with PCB-integrated cores are discussed in detail. The presented multi-objective design procedure determines the inductor and/or transformer setup optimal with respect to minimal losses and/or minimal footprint area; for this purpose, all required electrical, magnetic, and geometrical parameters of the magnetic component are considered in the design process. Furthermore, all specific implications entailed by the PCB-integrated core, e.g., the core setup, anisotropic core losses, the interleaving of windings, or an accurate reluctance model are treated. Finally, experimental results are used to verify the design procedure.
The Logic of Plausible Reasoning: A Core Theory
(1989)

1. o formal representation of plausible inference patterns; such OS deductions, inductions, ond analogies, thot ore frequently employed in answering every doy questions: 2. a set of parameters, such OS conditional likelihood, typicality, and similarity, that affect the certainty of people’s onswers to such questions; ond 3. a system relating the different plorisible inference patterns and the different certainty parameters.
A FPGA-Based Neuromorphic Locomotion System for Multi-Legged Robots
(2017)

The paper develops a neuromorphic system on a Spartan 6 field programmable gate array (FPGA) board to generate locomotion patterns (gaits) for three different legged robots (biped, quadruped, and hexapod). The neuromorphic system consists of a reconfigurable FPGA-based architecture for a 3G artificial neural network (spiking neural network), which acts as a Central Pattern Generator (CPG). The locomotion patterns, are then generated by the CPG through a general neural architecture, which parameters are offline estimated by means of grammatical evolution and Victor-Purpura distance-based fitness function. The neuromorphic system is fully validated on real biped, quadruped, and hexapod robots.
An Empirical Study of Algorithms for Point-Feature Label Placement
(1995)

A major factor affecting the clarity of graphical displays that include text labels is the degree to which labels obscure display features (including other labels) as a result of spatial overlap. Point-feature label placement (PFLP) is the problem of placing text labels adjacent to point features on a map or diagram so as to maximize legibility. This problem occurs frequently in the production of many types of informational graphics, though it arises most often in automated cartography. In this paper we present a comprehensive treatment of the PFLP problem, viewed as a type of combinatorial optimization problem. Complexity analysis reveals that the basic PFLP problem and most interesting variants of it are NP-hard. These negative results help inform a survey of previously reported algorithms for PFLP; not surprisingly, all such algorithms either have exponential time complexity or are incomplete. To solve the PFLP problem in practice, then, we must rely on good heuristic methods. We propose two new methods, one based on a discrete form of gradient descent, the other on simulated annealing, and report on a series of empirical tests comparing these and the other known algorithms for the problem. Based on this study, the first to be conducted, we identify the best approaches as a function of available computation time.
Mapping Agricultural Ecosystem Services across Scales
(2018)

Given the cross-scale interactions of agricultural ecosystems, it is important to collect ecosystem service data at the multiple spatial scales they operate at. Mapping of ecosystem services helps to assess their spatial and temporal distribution and is a popular communication tool of their availability and value. For example, maps can be used to quantify distance between areas of available ecosystem services and their beneficiaries and how services fluctuate with changes in land use patterns over time, allowing identification of synergies and trade-offs. However, a lack of local context and too large a resolution can reduce the utility of these maps, whilst masking heterogeneities in access due to equity dynamics. This review identifies and summarizes eight main methods of ESS mapping found in the literature – remote sensing, biophysical modelling, agent based modelling, economic valuation, expert opinion, user preference, participatory mapping, and photo-elicitation. We consider what spatial scales these methods are utilized at and the transferability of data created by each method. The analysis concludes with a methodological framework for mapping ecosystem services, intended to help researchers identify appropriate methods for a multi-scale research design. The framework is exemplified with an overview of a research project in Ethiopia.
Evidence for physiotherapy practice: a survey of the Physiotherapy Evidence Database (PEDro).
(2002)

Evidence-based practice involves the use of evidence from systematic reviews and randomised controlled trials, but the extent of this evidence in physiotherapy has not previously been surveyed. The aim of this survey is to describe the quantity and quality of randomised controlled trials and the quantity of systematic reviews relevant to physiotherapy. The Physiotherapy Evidence Database (PEDro) was searched. The quality of trials was assessed with the PEDro scale. The search identified a total of 2,376 randomised controlled trials and 332 systematic reviews. The first trial was published in 1955 and the first review was published in 1982. Since that time, the number of trials and reviews has grown exponentially. The mean PEDro quality score has increased from 2.8 in trials published between 1955 and 1959 to 5.0 for trials published between 1995 and 1999. There is a substantial body of evidence about the effects of physiotherapy. However, there remains scope for improvements in the quality of the conduct and reporting of clinical trials.
A Guided Tour Puzzle for Denial of Service Prevention
(2009)

Various cryptographic puzzle schemes are proposed as a defense mechanism against denial of service attack. But, all these puzzle schemes face a dilemma when there is a large disparity between the computational power of attackers and legitimate clients: increasing the difficulty of puzzles might unnecessarily restrict legitimate clients too much, and lower difficulty puzzles cannot sufficiently block attackers with large computational resources. In this paper, we introduce guided tour puzzle, a novel puzzle scheme that is not affected by such resource disparity. A guided tour puzzle requires a client to visit a predefined set of nodes, called tour guides, in a certain sequential order to retrieve an n piece answer, one piece from each tour guide that appears in the tour. This puzzle solving process is non-parallelizable, thus cheating by trying to solve the puzzle in parallel is not possible. Guided tour puzzle not only achieves all previously defined desired properties of a cryptographic puzzle scheme, but it also satisfies more important requirements, such as puzzle fairness and minimum interference, that we identified. The number of tour guides required by the scheme can be as few as two, and this extra cost can be amortized by sharing the same set of tour guides among multiple servers.
On-Chip Compensation of Ring VCO Oscillation Frequency Changes Due to Supply Noise and Process Variation
(2012)

A novel circuit technique that stabilizes the oscillation frequency of a ring-type voltage-controlled oscillator (RVCO) is demonstrated. The technique uses on-chip bias-current and voltage-swing controllers, which compensate RVCO oscillation frequency changes caused by supply noise and process variation. A prototype phase-locked loop (PLL) having the RVCO with the compensation circuit is fabricated with 0.13-μm CMOS technology. At the operating frequency of 4 GHz, the measured PLL rms jitter improves from 20.11 to 5.78 ps with 4-MHz RVCO supply noise. Simulation results show that the oscillation frequency difference between FF and SS corner is reduced from 63% to 6% of the NN corner oscillation frequency.
Learning Bayesian Belief Network Classifiers: Algorithms and System
(2001)

This paper investigates the methods for learning Bayesian belief network (BN) based predictive models for classification. Our primary interests are in the unrestricted Bayesian network and Bayesian multi-net based classifiers. We present our algorithms for learning these classifiers and also the methods for fighting the overfitting problem. A natural method for feature subset selection is also studied. Using a set of standard classification problems, we empirically evaluate the performance of various BN based classifiers. The results show that the proposed BN and Bayes multi-net classifiers are competitive with (or superior to) the best known classifiers, based on both BN and other formalisms; and that the computational time for learning and using these classifiers is relatively small. We also briefly introduce our BN classifier learning system – BN PowerPredictor. We argue that BN based classifiers deserve more attention in the data mining community.
Programmatically Interpretable Reinforcement Learning
(2018)

We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural “oracle”. We evaluate NDPS on the task of learning to drive a simulated car in the TORCS carracing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.
Estimator initialization in vision-aided inertial navigation with unknown camera-IMU calibration
(2012)

This paper focuses on motion estimation using inertial measurements and observations of naturally occurring point features. To date, this task has primarily been addressed using filtering methods, which track the system state starting from known initial conditions. However, when no prior knowledge of the initial system state is available, (e.g., at the onset of the system's operation), the existing approaches are not applicable. To address this problem, in this work we present algorithms for computing the system's observable quantities (platform attitude and velocity, feature positions, and IMU-camera calibration) directly from the sensor measurements, without any prior knowledge. A key contribution of this work is a convex-optimization based algorithm for computing the rotation matrix between the camera and IMU. We show that once this rotation matrix has been computed, all remaining quantities can be determined by solving a quadratically constrained least-squares problem. To increase their accuracy, the initial estimates are refined by an iterative maximum-likelihood estimator.
LKIM : The Linux Kernel Integrity
(2013)

509 he Linux Kernel Integrity Measurer (LKIM) is a next-generation technology for the detection of malicious modifications to a running piece of software. Unlike traditional antivirus systems, LKIM does not rely on a database of known malware signatures; instead, LKIM uses a precise model of expected program behavior to verify the consistency of critical data structures at runtime. APL and the Research Directorate of the National Security Agency (NSA) developed the LKIM prototype and are now working to transition the technology to a variety of critical government applications. LKIM: The Linux Kernel Integrity Measurer